PaperlayerView exact revision on arXiv

Source-linked AI summary

Cologne: A Declarative Distributed Constraint Optimization Platform

Changbin Liu, Lu Ren, Boon Thau Loo, Yun Mao, Prithwish Basu

arXiv:1204.6080v1cs.DB

TL;DR

Distributed systems need maintainable ways to optimize resources and policies under constraints across multiple nodes. Cologne addresses this gap with the Colog language, combining distributed Datalog evaluation with constraint solving, and reports broad policy support, much smaller programs, and efficient distributed execution. The paper demonstrates the approach in cloud and wireless-network case studies.

  • Problem

    Distributed constraint optimizations are difficult to maintain in imperative languages, often require thousands of lines, and may need coordinated execution across administrative domains.

  • Method

    Cologne integrates a declarative networking engine with an off-the-shelf constraint solver, while Colog combines distributed Datalog with goal-and-constraint constructs and mixed bottom-up and top-down execution.

  • Results

    Cologne supports a wide range of policy-based distributed optimizations, produces orders of magnitude less code than imperative implementations, and achieves low overhead with fast convergence.

  • Takeaways & Limitations

    The cloud and wireless case studies show that Cologne can customize distributed-system optimization policies across distinct deployment scenarios.

Abstract

from arXiv · show

This paper presents Cologne, a declarative optimization platform that enables constraint optimization problems (COPs) to be declaratively specified and incrementally executed in distributed systems. Cologne integrates a declarative networking engine with an off-the-shelf constraint solver. We have developed the Colog language that combines distributed Datalog used in declarative networking with language constructs for specifying goals and constraints used in COPs. Cologne uses novel query processing strategies for processing Colog programs, by combining the use of bottom-up distributed Datalog evaluation with top-down goal-oriented constraint solving. Using case studies based on cloud and wireless network optimizations, we demonstrate that Cologne (1) can flexibly support a wide range of policy-based optimizations in distributed systems, (2) results in orders of magnitude less code compared to imperative implementations, and (3) is highly efficient with low overhead and fast convergence times.

1. INTRODUCTION

Cologne addresses the difficulty of maintaining imperative distributed constraint optimizations by combining declarative networking with constraint solving. Its Colog language and distributed execution support broad policy-based optimizations with substantially less code and efficient convergence.

  • Motivation: Cologne declaratively specifies and incrementally executes constraint optimization problems in distributed systems, addressing the maintenance burden of imperative implementations.Traditional C++ or Java implementations can require thousands of lines and face distributed execution and scalability requirements.
  • Declarative platform: Colog combines distributed Datalog with language constructs for specifying optimization goals and constraints.The platform integrates a declarative networking engine with an off-the-shelf constraint solver.
  • Distributed constraint optimizations: Cologne combines incremental bottom-up distributed Datalog evaluation with top-down goal-oriented constraint solving for distributed constraint optimizations.The integration uses RapidNet and Gecode without modifying either system, making the techniques generic to distributed Datalog engines and constraint solvers.
  • Use cases: Cologne is applied to cloud resource orchestration and wireless channel-selection policies, demonstrating use across distinct distributed-system scenarios.The cloud case manages resources under customer and provider objectives, while the wireless case optimizes channel policies.
  • Evaluation: The evaluation reports flexible policy support, orders of magnitude less code than imperative implementations, and low-overhead, fast-converging execution.These claims summarize the prototype evaluation across the presented use cases.

2. SYSTEM OVERVIEW

Cologne supports centralized and distributed deployments that gather system state, apply policy goals and constraints, and generate configuration commands. Distributed instances exchange states and optimization outputs while allowing administrative and computational decomposition across nodes.

  • Deployment modes: Cologne can operate in centralized or distributed mode within a network of configured nodes.The distributed mode typically places one Cologne instance at each node.
  • Centralized deployment: In centralized mode, one Cologne instance feeds global system states and Colog policies into a solver that generates commands for each node.The commands enter each node’s configuration layer to produce physical resource operations.
  • Distributed deployment: In distributed mode, a distributed query engine coordinates system states and optimization outputs among Cologne instances to pursue a global objective.Nodes communicate with directly reachable neighbors over wireless links, backbones, or the Internet.
  • Deployment rationale: Distributed deployment supports federated administration because providers can configure internally while coordinating inter-data-center configurations.This arrangement accommodates separate administrative domains.
  • Deployment rationale: Distributed deployment can also partition constraint optimization because each node configures a smaller resource set using local optimization commands.The stated motivation is scalability even when the system belongs to one administrative domain.
  • Configuration layer: The configuration layer depends on the use case, representing cloud resource controllers in data centers or routing and channel configuration in wireless nodes.Cologne’s optimization interface is therefore connected to domain-specific resource manipulation.

3. USE CASE EXAMPLES

Cologne models cloud orchestration and wireless configuration as constraint optimization problems with customizable goals and constraints. The examples show how it combines resource, communication, migration, and interference considerations in distributed settings.

  • Overview: The use cases cover cloud resource orchestration and wireless network configuration, spanning across-data-center and wireless-node deployment scenarios.They are intended to demonstrate Cologne’s applicability across substantially different environments.
  • Cloud orchestration: Cologne models cloud resources and orchestration decisions as COPs whose goals and constraints automatically produce orchestration commands.Each cloud controller uses a constraint solver and distributed query engine to coordinate resources across data centers.
  • ACloud: ACloud replaces ad-hoc VM placement and migration with policies over real-time CPU load, memory load, and migration feasibility.Supported objectives include reducing cluster-wide load variance, limiting migrations, or consolidating workloads subject to resource and security constraints.
  • Follow-the-Sun: Follow-the-Sun migrates VMs across geographically distributed data centers as customer demand shifts during the day.The model represents capacities, demands, allocations, migration feasibility, and customer-preferred locations.
  • Follow-the-Sun: The Follow-the-Sun COP minimizes aggregate operating, communication, and VM migration costs.The formulation sums aggOC, aggCC, and aggMC while respecting resource-capacity and migration-balance constraints.
  • Wireless network configuration: Wireless channel-selection policies are expressed as Colog COPs that can flexibly control local and global protocols in centralized or distributed deployments.The broader network objective includes dynamic spectrum access, channel selection, medium access, and routing for overall network performance.

4. COLOG LANGUAGE

Colog extends Datalog with goals, variables, constraints, and distributed computation constructs for declaratively specifying constraint optimization problems. Its language separates regular database facts from solver-derived attributes and supports distributed, customizable policies.

  • Language foundations: Colog combines Datalog’s declarative rules with constructs for goals, solver variables, constraints, and distributed computations.The language is designed to express dependencies among system states while formulating optimization objectives and restrictions.
  • Attribute and table types: Regular attributes obtain values from database facts, whereas solver attributes are determined by the constraint solver after optimization.Tables containing solver attributes are solver tables; tables containing only regular attributes remain traditional Datalog tables.
  • Solver rules: Solver derivation rules derive new solver variables, while solver constraint rules restrict allowed values and encode invariants for search.Derivation rules use <- syntax, whereas constraint rules use -> syntax and constrain existing solver attributes.
  • Centralized example: A centralized Colog load-balancing program minimizes host CPU standard deviation by assigning each VM to a host under assignment and memory constraints.The program uses solver variables for assignments, aggregates host CPU and memory usage, and enforces one-host-per-VM and capacity conditions.
  • Distributed Colog: Distributed Colog uses location specifiers to express rules over data spanning nodes and decomposes optimization into per-node constraint problems.Nodes expose limited information to neighbors, supporting autonomy and decomposition of larger optimization problems.
  • Policy customization: Colog’s declarative rules allow policies such as migration limits or post-optimization cost thresholds to be added through constraints.These policies can be customized without replacing the underlying optimization formulation with imperative code.

5. EXECUTION PLAN GENERATION

Cologne compiles Colog programs into execution plans by combining RapidNet’s incremental distributed Datalog evaluation with Gecode constraint solving. Static analysis identifies solver rules, while distributed rewrites and solver invocations coordinate computation across nodes.

  • Architecture: Cologne’s compiler and runtime integrate a distributed query processor with an off-the-shelf constraint solver to execute Colog programs.The implementation uses RapidNet and Gecode, although the techniques are intended to generalize to other engines and solvers.
  • General rule evaluation: RapidNet evaluates distributed Datalog incrementally through pipelined semi-naïve evaluation and incremental view maintenance.Rules run continuously as tuples arrive, updating rule heads rather than recomputing them from scratch.
  • Solver integration: Solver derivation rules invoke Gecode to optimize variables under constraints, and optimization outputs are materialized as RapidNet tables.Materialized outputs can trigger incremental reevaluation of other rules.
  • Solver-rule identification: Static analysis identifies solver attributes from var declarations and rule dependencies, then classifies rules as solver derivations or constraints.The analysis propagates solver-attribute status transitively and distinguishes rule types by <- versus -> syntax.
  • Solver-rule processing: Solver derivations reuse Datalog query operators but translate expressions involving solver attributes into Gecode constraints.For example, aggregation over solver attributes creates a constrained sum variable.
  • Distributed plan generation: Distributed plan generation uses RapidNet to move intermediate tuples between nodes, while centralized solver derivation rules execute after the distributed rewrite.Location specifiers determine where data is generated and sent, allowing distributed and solver processing to be combined.

6. EVALUATION

Cologne’s evaluation combines cloud and wireless use cases to assess code compactness, optimization quality, policy flexibility, and distributed execution overhead. Across these experiments, the platform produces compact implementations, effective optimization, low communication overhead, and fast convergence, subject to solver scalability limits.

  • Compactness: The generated imperative code is approximately 100X the size of equivalent Colog programs.The comparison includes all rules needed for Gecode solving and RapidNet distributed communication.
  • ACloud: 98.1% and 87.8% reductions in CPU load imbalance were achieved relative to Default and Heuristic, respectively, in the ACloud evaluation.ACloud evaluated average CPU standard deviation across three data centers over four hours.
  • ACloud: 20.3 VM migrations per interval fell to 9 per interval when ACloud used a migration constraint.The constrained configuration performed three migrations per data center.
  • Follow-the-Sun: Follow-the-Sun reduced normalized total cost by 40.4% to 11.2% as the number of data centers increased from 2 to 10.Cost reduction became less apparent for larger networks because distributed solving approximates the optimal solution and the search space grows exponentially.
  • Follow-the-Sun: A migration-limited Follow-the-Sun policy reduced VM migrations by 24% on average while maintaining comparable cost-reduction ratios and convergence times.The policy limits migrations between any two data centers to 20 or fewer.
  • Overhead and convergence: At 10 data centers, per-node communication overhead was about 3.5KBps and grew linearly with network size.Per-link COP computations completed within 0.5 seconds on average, while larger networks required more negotiation rounds to converge.
  • Wireless channel selection: Centralized and distributed Cologne wireless protocols significantly outperformed single-interface and identical-channel assignments, while the cross-layer protocol had the best overall throughput and loss-rate performance.In the 30-node evaluation, centralized and distributed protocols showed consistent relative differences and scalability trends.

7. RELATED WORK

Cologne extends prior declarative optimization efforts into a general-purpose distributed platform with Colog and integrated distributed query processing and constraint solving.

  • Cologne generalizes earlier specialized platforms for cloud resource orchestration and wireless network configuration into a general framework, language, and compilation techniques.
  • Unlike prior systems, Cologne targets general declarative distributed constraint optimization with a user-friendly policy language and orders-of-magnitude code-size reduction versus imperative alternatives.
  • Colog supports distributed optimization by integrating a distributed query engine with a constraint solver.
  • Cologne supports simulation and physical deployment, allowing distributed COP execution to be tested in a controllable network environment before deployment on real devices.

8. CONCLUSION

The paper presents Cologne as a declarative distributed constraint-optimization platform, evaluates it on cloud and wireless use cases, and reports feasibility across supported policies and platform efficiency.

  • Cologne combines Colog, novel solver-aware distributed query processing, and compilation techniques with RapidNet and Gecode in a complete prototype.
  • The platform supports customizable policies for cloud resource orchestration and wireless network configuration.
  • Evaluation results demonstrate Cologne’s feasibility in supporting a wide range of policies and operating efficiently.
  • Future work will explore distributed COP applications including decentralized data analysis, model fitting, resource allocation, and network optimization.

A. DECLARATIVECHANNEL SELECTION

The wireless channel-selection case study formulates channel assignment as a constraint optimization problem and provides equivalent centralized and distributed Colog programs.

  • Wireless channel selection is first formulated as a constraint optimization problem and then expressed through centralized and distributed Colog programs.

A.1 COP Formulation

The formulation assigns channels to links in a wireless network to reduce interference, subject to channel-availability, link-consistency, and interface-count constraints.

  • Channel assignment chooses a channel for each link to minimize interference among conflicting links, corresponding to a graph-coloring problem.
  • Under the one-hop interference model, adjacent links interfere when their channel frequency bands are closer than a specified threshold.
  • The network is represented as G = (V, E), with nodes, edges, locally occupied primary-user channels, and interface counts as inputs.
  • The formulation excludes channels occupied by primary users, requires adjacent communicating nodes to share a channel, and limits assignments to available radio interfaces.

A.2 Centralized Channel Selection

The centralized channel-selection program minimizes interference across communication links while enforcing primary-user, symmetry, and interface constraints. Colog also adapts the policy to a two-hop interference model by adding one neighborhood predicate.

  • Constraints: The constraints prohibit channels used by primary users, enforce symmetric assignments, and limit each node to its available number of interfaces.These are encoded by the primary-user, channel-symmetry, and interface constraints.
  • Optimization goal and variables: The optimization minimizes total interference cost while assigning a channel variable to every communication link.The assign(X,Y,C) relation records the channel used between X and Y, and totalCost aggregates interference costs.
  • Cost derivation: Interference cost is one for adjacent links using channels closer than the minimum frequency difference and zero otherwise.Rule d1 compares channel assignments, while d2 sums the resulting costs across the network.
  • Two-hop interference: The two-hop interference model counts interference from similar channels on links within two hops of one another.It extends the one-hop cost rule to four adjacent nodes and adds a neighbor predicate.
  • Policy customization: Colog supports both one-hop and two-hop interference policies by adding only one link predicate to the original cost rule.This demonstrates that the channel-selection policy can be customized without replacing the overall optimization structure.

A.3 Distributed Channel Selection

The distributed channel-selection program performs local COP computations for one negotiated link at a time and propagates resulting assignments to neighbors. Unlike centralized search over all links, it uses neighborhood information and therefore produces an approximation.

  • Distributed execution: Distributed Colog computes channel assignments from local neighborhood information and exchanges them with neighboring nodes for further computation.This supports distributed COP execution rather than a single global solver run.
  • Negotiation process: The protocol periodically selects one link, negotiates its channel with a neighbor, and stores the resulting assignment.The selected link is represented by setLink(@X,Y).
  • Local optimization: Each negotiation minimizes the local total interference cost for the selected link while assigning its channel.The goal and variable declarations restrict optimization to assign(@X,Y,C) for the negotiated link.
  • Distributed policy: The distributed rules use the two-hop interference model, primary-user constraints, and channel propagation to neighboring links.The cost rule examines neighboring links, constraints avoid primary-user channels, and r1 restores assignment symmetry.
  • Scope and approximation: Unlike centralized search over all link assignments, distributed execution updates one link at a time using only local neighborhood information.Consequently, each COP execution is an approximation based on the node’s neighborhood rather than a global search.
Loading 1204.6080v1…