Adaptive Behavior · 2004 · 291 citations · 29 references
Load Balancing (Computing)EngineeringDynamic Resource AllocationServer Allocation OptimizationCloud Load BalancingDynamic Server AllocationInternet ComputingHoney BeesMechanism DesignLoad BalancingDistributed Resource ManagementFair Resource AllocationComputer ScienceCloud ComputingBusinessResource AllocationInternet CentersHoney Bee AlgorithmInternet Hosting CentersContent Delivery Network
Internet hosting centers must allocate a limited number of servers among clients to maximize revenue, but unpredictable request patterns and reallocation costs make optimization difficult. The study proposes a decentralized honey‑bee algorithm to dynamically allocate servers in hosting centers. The algorithm is evaluated against optimal, greedy, and static allocation baselines using simulated and commercial request traces. The honey‑bee algorithm outperforms static and greedy strategies under highly variable loads, though greedy can win under low variability, highlighting its responsiveness to load changes.
Internet centers host services for e-banks, e-auctions and other clients. Hosting centers then must allocate servers among clients to maximize revenue. The limited number of servers, costs of reallocating servers, and unpredictability of requests make server allocation optimization difficult Based on the many similarities between server and honey bee colony forager allocation, we pro pose a new decentralized honey bee algorithm which dynamically allocates servers to satisfy request loads. We compare it against an omniscient optimality algorithm, a conventional greedy algorithm, and an algorithm that computes omnisciently the optimal static allocation. We evaluate performance on simulated request streams and commercial trace data Our algorithm performs better than static or greedy for highly variable request loads, but greedy can outperform it under low variability. Honey bee forager allocation, though suboptimal for static food sources, may possess a counterbalancing responsiveness to food source variability.
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