Dynamic Service Contract Enforcement in Service-Oriented Networks

Abstract

International audienceIn recent years, Service Oriented Architectures (SOA) have emerged as the main solution for the integration of legacy systems with new technologies in the enterprise world. A service is usually governed by a client service contract (CSC) that specifies, among other requirements, the rate at which a service should be accessed, and limits it to no more than a number of service requests during an observation period. Several approaches, using both static and dynamic, credit-based strategies, have been developed in order to enforce the rate specified in the CSC. Existing approaches have problems related to starvation, approximations used in calculations, and rapid credit consumption under certain conditions. In this paper, we propose and validate DoWSS, a doubly-weighted algorithm for service traffic shaping. We show via simulation that DoWSS possesses several advantages: it eliminates the approximation issues, prevents starvation and contains the rapid credit consumption issue in existing credit-based approaches

Towards Multi-Service Traffic Shaping in Two-Tier Enterprise Data Centers

“…We also assume that the processing rate of each document at each appliance varies and depends directly on request sizes. Previous works have explored the responsiveness of creditbased algorithms [2], [3]. Results show that for T = 1 and K = 40, the algorithm achieves a reasonable responsive behavior.…”

“…Several approaches, using both static and dynamic creditbased strategies, have been developed in order to enforce the rate specified in the CSC [2], [3]. Nevertheless, these solutions have so far only considered the multipoint-to-singlepoint case where a cluster of SON Appliances shapes service traffic toward a single service instance.…”

Service Contracts: Current State and Future Directions

“…QoD QoS Legal Cont Busine issue -ext -ss term Jarma .et [15] X V X X X Wrighta and Fergussonb [30] X V X V V Bochicchio and Longo [5] X V V V V Meiera .et [16] V X X X V Zou .et [32] X V X X V Homburg and Stebel [14] X V X X V Simoni .et [22] X V V X X Comuzzi and Pernici [10] V V X V V Bravetti and Zavattaro [6] X X V X X Padovani [19] X V X X X Burbon [7] V V V V V Telang and Singh [24] X X X X V Zimmermann [31] X V X X X Gao .et [13] X X V V X Berberova and Bontchev [3] V V X X V Datta and Roy [11] X V X X V Abu-Matar and Gomaa [1] X V X X V Truong .et [28] V V V V V Andrikopoulos .et [2] X V V X X Bicer .et [4] X X V X X Fenech .et [12] X V X X V Spillner .et [23] X V V X V Muller .et [17] X V V X V Nepal and Zic [18] V V V X V Panziera .et [21] and Palmonari .et [20] Empirically we adopt the following dimensions: QoD: multiple metrics which are used to describe data quality,e.g., completeness, reliability (information), accuracy, consistency, timeliness, cleaning, and interpretability [25];…”

Efficient QoS management for QoS-aware web service composition

“…Then a service broker is used to help a customer select multiple web services (with different functional attributes but same Quality of Service (QoS)) to composite a new value-added service according to her QoS requirements. Hence, how to manage efficiently QoS of these web services is a critical problem in web service composition area (Jarma et al, 2013).…”