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Tuah, Nor Jaidi; Kumar, Mohan; Venkatesh, Svetha

doi:10.1023/a:1020829124143

Cited by 18 publications

(4 citation statements)

References 14 publications

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“…The results of the Wald test to evaluate the null hypothesis of k 1 = k 2 = _ = k 12 , yields an F value of 27.722. The null .023540 h 6 .648036 h 7 .886481 h 8 .225282 h 9 .902247 h 10 .521423 h 11 .220639 h 12 .636066 k .000001 f .000000 .000290 # f 0 .000007 .044617 à b 1,8 .044893 à b 1,14 .046623 # b 1,15 .045322 # b 7, 16 -.038984 # b 9, 16 -.038324 # b 7, 18 -.027254 # b 9, 18 -.025562 # b 7, 19 -.024061 à b 9, 19 -.024353 à c 7,1 -.013037 à c 1,2 .025986 à c 11,2 -.013052 à c 7,3 -.002352 à c 1,5 .024671 # g 1 0.078578 g 2 0.089664 g 3 0.072775 g 4 0.081545 g 5 -0.055284 g 6 0.110166 g 7 0.218938 g 9 0.176231 g 10 0.146975 g 11 -0.033631 g 12 0.101146 hypothesis is rejected, implying that the time trends do vary across the panels. Note however that while the coefficients are statistically significant and do vary across panels, the coefficient values themselves are very small.…”

Section: Results and Analysismentioning

confidence: 98%

“….000841 b 8 .007423 à b 9 .004065 b 10 -.001830 b 11 -.002200 b 12 .000905 b 13 .005420 b 14 .000686 b 15 .003935 b 16 -.008270 à b 17 .001725 b 18 -.003970 b 19 .004235 b 20 .…”

Section: Results and Analysismentioning

confidence: 99%

“…Web caching/content replication is a special and popular type of storage, where frequently accessed content is (often automatically) stored at places close to users, and is frequently refreshed based on usage [12]. There has been some research on cache design and performance evaluation, including differentiated content caching services [13], integration of cache replacement and pre-fetching [14], use of proxies to perform transcoding as well as caching for efficient rich media delivery to heterogeneous network users [15], and client-side server selection algorithms [16]. The proposed discounted capacity sharing concept is directly applicable in these contexts; this approach would lead to market mechanisms that enhance resource utilization and generate potential revenues through trades among a set of cooperative resource-sharing service providers in a CPN.…”

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confidence: 99%

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Topographically discounted Internet infrastructure resources: a panel study and econometric analysis

Geng

Gopal

et al. 2007

Inf Technol Manage

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The Internet infrastructure is increasingly utilized for the provision and delivery of a variety of services. The rapid growth and substantial demand fluctuations of the supported services are placing strain on the service providers to effectively manage their infrastructure resources. Capacity Provision Network (CPN) is a marketdriven mechanism that enables the service providers to efficiently allocate, share and trade service capabilities. Central to the operation of CPN is the notion of discounted capacities which capture the underlying topology of the Internet. It serves as a practical instrument for service providers to evaluate the utility of capacity services available at remote sites vis-à-vis the capacities available locally and from the content providers. We conducted an Internet based longitudinal field experiment to empirically assess and validate the critical properties of discount factors. Our analysis reveals that discount factors exhibit markedly lower volatility than network delays, and are more stable over seasonal and temporal trend patterns. The results related to size effects suggest that sharing and trade agreements with larger content can be particularly effective. Our finding on the existence of the concavity property points to the ability of intermediation services to enhance the overall gains from resource sharing arrangements.The provision and delivery of a variety of services over the Internet are advancing at a rapid pace. The service offerings are continually evolving with numerous types of new services that replace or augment existing services for two primary reasons: (i) attaining higher cost-effectiveness and quality in service provisioning and delivery, and (ii) taking advantage of new business opportunities that arise due to the developments in technology, business environments and markets.One major challenge in Internet-based service provision is on how to control delay when service providers (SP) experience substantial demand fluctuations, possibly over different locations and periods. Two leading approaches to solving the network delay issue are (i) increasing available network bandwidth and responsiveness so that network delay can be contained even if traffic shoots up substantially, and (ii) pre-pulling content closer to users so that, when demanded, content travels a shorter path. One prominent example of the former approach is the constant

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Section: Results and Analysismentioning

confidence: 98%

“….000841 b 8 .007423 à b 9 .004065 b 10 -.001830 b 11 -.002200 b 12 .000905 b 13 .005420 b 14 .000686 b 15 .003935 b 16 -.008270 à b 17 .001725 b 18 -.003970 b 19 .004235 b 20 .…”

Section: Results and Analysismentioning

confidence: 99%

mentioning

confidence: 99%

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Topographically discounted Internet infrastructure resources: a panel study and econometric analysis

Geng

Gopal

et al. 2007

Inf Technol Manage

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“…Much research has gone into data management [5], prefetching [6], [7], caching [8], consistency [9], and opportunistic transfer [10], [11] of data in pervasive systems. While much of this work can still be leveraged to improve the interface between pervasive and cloud systems, it becomes less of an essential portion of the system.…”

Section: B Data Managementmentioning

confidence: 99%

Challenges in securing the interface between the cloud and pervasive systems

Lagesse

2011

2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)

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Abstract-Cloud computing presents an opportunity for pervasive systems to leverage computational and storage resources to accomplish tasks that would not normally be possible on such resource-constrained devices. Cloud computing can enable hardware designers to build lighter systems that last longer and are more mobile. Despite the advantages cloud computing offers to the designers of pervasive systems, there are some limitations of leveraging cloud computing that must be addressed. We take the position that cloud-based pervasive system must be secured holistically and discuss ways this might be accomplished.In this paper, we discuss a pervasive system utilizing cloud computing resources and issues that must be addressed in such a system. In this system, the user's mobile device cannot always have network access to leverage resources from the cloud, so it must make intelligent decisions about what data should be stored locally and what processes should be run locally. As a result of these decisions, the user becomes vulnerable to attacks while interfacing with the pervasive system.

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