The ACM SIGCHI community has been at the forefront of addressing issues of equity and inclusivity in the design and use of technology, accounting for various aspects of users' identities such as gender, ethnicity, and sexuality. With this panel, we wish to explore how we, as SIGCHI, might better target similar goals of equity and inclusivity-across intersections-within our own community. We wish to create a forum for recognizing best practices regarding equity and inclusivity in participants' local and global contexts that we might feasibly integrate across SIGCHI. By equally prioritizing the voices of those in the audience and on the panel, we intend to foster a lively and constructive discussion that will help us chart a way forward. The takeaways from this panel will be articulated into an article for the Interactions magazine, targeting the larger human-computer interaction (HCI) community.

Abstract-Providing resource allocation with performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, in which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. Existing resource allocation solutions either assume that applications manage their data transfer between their virtualized resources, or that cloud providers manage their internal networking resources. With the increased prevalence of brokerage services in cloud platforms, there is a need for resource allocation solutions that provides predictability guarantees in settings, in which neither application scheduling nor cloud provider resources can be managed/controlled by the broker. This paper addresses this problem, as we define the Network-Constrained Packing (NCP) problem of finding the optimal mapping of brokered resources to applications with guaranteed performance predictability. We prove that NCP is NP-hard, and we define two special instances of the problem, for which exact solutions can be found efficiently. We develop a greedy heuristic to solve the general instance of the NCP problem , and we evaluate its efficiency using simulations on various application workloads, and network models.

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Existing mobile devices roaming around the mobility field should be considered as useful resources in geo-temporal request satisfaction. We refer to the capability of an application to access a physical device at particular geographical locations and times as GeoPresence, and we presume that mobile agents participating in GeoPresence-capable applications should be rational, competitive, and willing to deviate from their routes if given the right incentive. In this paper, we define the Hitchhiking problem, which is that of finding the optimal assignment of requests with specific spatio-temporal characteristics to competitive mobile agents subject to spatio-temporal constraints. We design a mechanism that takes into consideration the rationality of the agents for request satisfaction, with an objective to maximize the total profit of the system. We analytically prove the mechanism to be convergent with a profit comparable to that of a 1/2-approximation greedy algorithm, and evaluate its consideration of rationality experimentally.