Reconfigurable TCP: An Architecture for Enhanced Communication Performance in Mobile Cloud Services

Naoki, Oguchi; Abe, Shunji

doi:10.1109/saint.2011.45

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Moreover, overall performance of large-scale distributed data processing is known to degrade when the system contains nodes that have a large communication delay [13]. For loosely coupled communication applications such as mpiBLAST, we can always see a considerable speedup as here what prevails is the computation gained by an increased parallelism over the communication overhead.…”

Section: Introductionmentioning

confidence: 99%

“…(1) begin (2) Data: (the set of tasks); (3) (communication graph); (4) (the set of processors); (5) (the background load of processors); (6) Result: M: → ; // A task mapping (7) nObjs ← NumObjs; // number of migratable objects (8) ObjectHeap objHeap(nObjs + 1); // declare a object heap objHeap (9) → maxhAllObjs; // max for all migratable objects (10) ProcessorHeap lightProcessors(P); // declare a processor heap lightProcessors (11) → lightProcessors; (12) for ← 1 to nObjs do (13) minLoad ← MAX DOUBLE; // Initially the minimum load minLoad are set to MAX DOUBLE (14) o ← objHeap.deleteMax(); // Find the object o with high loads (15) cpuDonor ← lightProcessors->deleteMin(); // Find the migratable target (processor) with light loads (16) ← ∑ ∈ ∧ ( )= ( ∧ ); // comm cost represents communication cost (17) newLoad ← cpuDonor.load + comm cost; // Recalculate new load considering communication cost (18) if newload < minLoad then (19) minLoad ← newLoad; (20) newDonor ← cpuDonor; // Find new migratable processor (21) end if (22) for cpuDonor ∈ comm do (23) ← ∑ ∈ ∧ ( )= ( ∧ ); (24) newLoad ← cpuDonor.load + comm cost; (25) if newLoad < minLoad then (26) minLoad ← newLoad; (27) newDonor ← cpuDonor; // newDonor: new migratable processor (28) end if (29) end (3) Overhead Calculation. The overhead of each leaf node can be represented as…”

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confidence: 99%

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A Hierarchical Load Balancing Strategy Considering Communication Delay Overhead for Large Distributed Computing Systems

Yang

Ling

Liu

2016

Mathematical Problems in Engineering

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Load balancing technology can effectively exploit potential enormous compute power available on distributed systems and achieve scalability. Communication delay overhead on distributed system, which is time-varying and is usually ignored or assumed to be deterministic for traditional load balancing strategies, can greatly degrade the load balancing performance. Considering communication delay overhead and its time-varying feature, a hierarchical load balancing strategy based on generalized neural network (HLBSGNN) is presented for large distributed systems. The novelty of the HLBSGNN is threefold: (1) the hierarchy with optimized communication is employed to reduce load balancing overhead for large distributed computing systems, (2) node computation rate and communication delay randomness imposed by the communication medium are considered, and (3) communication and migration overheads are optimized via forecasting delay. Comparisons with traditional strategies, such as centralized, distributed, and random delay strategies, indicate that the HLBSGNN is more effective and efficient.

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Section: Introductionmentioning

confidence: 99%